The software development life cycle (SDLC) proceeds through multiple stages of development and testing. Illustrative stages include a development stage, a component testing stage, a system testing stage, a functional testing stage and a production stage. At each stage, specific requirements must be satisfied in order to deem the stage to be complete. These requirements may be reviewed in a series of internal and external audits.
Due to the number of audits that must be completed over the course of the SDLC, the audit process is often plagued with oversight and error. Moreover, each stage may involve different stakeholders, with different allocations of responsibility. The involvement of multiple stakeholders complicates assignment of responsibility for audit failures and makes it difficult to identify and implement comprehensive remediation measures.
The involvement of multiple stakeholders also makes it difficult to evaluate patterns behind failures and assess the effectiveness of remediation strategies. These difficulties in turn impede the development of proactive protocols designed to address specific weaknesses in the SDLC.
It would be desirable, therefore, to implement a comprehensive review encompassing audit requirements for all stages of the software development lifecycle. It would further be desirable to integrate detection of audit failures with remediation measures. It would further be desirable to combine the comprehensive review with machine learning to identify and implement changes that will improve efficiency and reduce audit failures.